This document compares two typology construction approaches—statistical and participatory—for categorizing farm households in northern Ghana. It highlights that while statistical methods provide objectivity, participatory methods capture local context, though both face limitations in accuracy and overlap in classifications. The authors recommend using both approaches complementarily to enhance the relevance and effectiveness of agricultural interventions.

1. A comparison of farm typology approaches in northern Ghana
Katja Kuivanen, Stéphanie Alvarez, Mirja Michalscheck, Samuel Adjei-Nsiah, Katrien Descheemaeker, Shaibu Bedi Mellon, Jeroen Groot
Key messages
Grouping diverse farm households into subsets or ideal types can
support the development (selection of farms), implementation
(targeting and scaling-out of innovations) and monitoring (scaling up
of impact assessments) of R4D projects. Different approaches to
typology construction can have different results and this will affect
the meaningfulness of the resulting types for involved stakeholders.
 The statistical typology revealed general structure/ pattern of
farm household variation: possibility for extrapolation beyond
study area.
 By incorporating actor perspectives, the participatory typology
captured context-specific aspects of farm complexity: may
enhance local relevance and socio-cultural sensitivity of
interventions.
This poster is licensed for use under the Creative Commons Attribution 4.0 International Licence.
January 2017
We thank farmers and local partners in Africa RISING sites for their contributions to this research. We also acknowledge
the support of all donors which globally support the work of the CGIAR centers and their partners through their
contributions to the CGIAR system
Objectives and approach
The aim of this study was to compare two approaches for typology
construction for 80 surveyed farm households across three Africa
RISING project communities in Ghana’s Northern Region: statistical
(top-down, researcher-defined) and participatory (community-based,
farmer-defined), see Figure 1.
Key results
 Selection differed between statistical (12 quantitative key
variables comprising household size, labour, land use, livestock
and income) and participatory groupings (15 criteria - farm size
and gender most discriminating), see Table 1.
 Similar descriptive names belied divergent underlying meaning
due to interpretation/cultural differences (Table 1; Figure 2).
 For both typologies data were sometimes inaccurate: for the
statistical typology survey data did not always reflect the reality
(for various reasons), for the participatory typology cultural and
social (power) issues tended to distort assessment of farms.
 Limited overlap between assignment of surveyed households to
types when comparing the two typologies.
 Nevertheless, mean profile of participatory types roughly matches
that of corresponding statistical types on selected variables.
Type Symbol Main characteristics % in survey
Statistical Typology
1 HRE, large cattle herd, ample off-farm activities 11%
2 MRE, large farms, market orientation 10%
3 MRE, small ruminants, on-farm labor intensive 13%
4 MRE, small ruminants, ample hired labor 46%
5 LRE, maize dominated, few off-farm activities 14%
6 SRC, livestock sales, ample off-farm activities 6%
Participatory typology
A Pukparkara (‘Big farmers’- men): HRE, market-orientation
8%
B Pukparsagsa (‘Medium farmers’- men): MRE, variable orientation 52%
C Pukparbihi (‘Small farmers’- men): LRE, subsistence orientation 40%
D
Pagba pubihi (‘Small farmers’- women & children): LRE and SRC, market
orientation
0%
E Suhukpion (‘Farm-less’- men): work on other farms as hired labour 0%
Significance and scaling potential
Although statistical techniques warrant objectivity and reproducibility
in the analysis, the complexity of data collection and representation
of the local reality might limit their effectiveness in selection of
farms, innovation targeting and out-scaling in R4D projects. In
addition, while participatory typologies offer a more contextualized
representation of heterogeneity, their accuracy can still be
compromised by socio-cultural constraints. Therefore, we
recommend making effective use of the advantages offered by each
approach by applying them in a complementary manner.
Figure 1. Steps in the analysis comparing statistical and participatory typology construction methods
applied in northern Ghana.
Table 1. Main characteristics of the five farm types determined using participatory methods (HRE: High
resource endowed; MRE: Medium resource endowed; LRE: Low resource endowed; SRC: Severely
resource constrained).
Figure 2. Classificatory overlap between the typologies (A) and kernel density curves (participatory Types
A-C, group means represented by colored dashed lines) combined with boxplots (statistical Types 1-6,
group means represented by black markers) for the variable of herd size (total TLU) per Type (B).
Kuivanen KS, Alvarez S, Michalscheck M, Adjei-Nsiah S, Descheemaeker K, Mellon-Bedi S, Groot JCJ 2016 Characterising the
diversity of smallholder farming systems and their constraints and opportunities for innovation: a case study from the
Northern Region, Ghana. NJAS Wageningen J Life Sci 78 153.
Kuivanen KS, Michalscheck M, Descheemaeker K, Adjei-Nsiah S, Mellon-Bedi S, Groot JCJ, Alvarez S 2016 A comparison of
statistical and participatory clustering of smallholder farming systems – A case study in Northern Ghana. J Rural Stud 45 184.